Modulating Co-Stimulation During Antigen Presentation to Enhance Cancer Immunotherapy
Bibliographic record
Abstract
One of the key roles of the immune system is the identification of potentially dangerous pathogens or tumour cells, and raising a wide range of mechanisms to eliminate them from the organism. One of these mechanisms is activation and expansion of antigen-specific cytotoxic T cells, after recognition of antigenic peptides on the surface of antigen presenting cells such as dendritic cells (DCs). However, DCs also process and present autoantigens. Therefore, antigen presentation has to occur in the appropriate context to either trigger immune responses or establishing immunological tolerance. This is achieved by co-stimulation of T cells during antigen presentation. Co-stimulation consists on the simultaneous binding of ligand-receptor molecules at the immunological synapse which will determine the type and extent of T cell responses. In addition, the type of cytokines/chemokines present during antigen presentation will influence the polarisation of T cell responses, whether they lead to tolerance, antibody responses or cytotoxicity. In this review, we will focus on approaches manipulating co-stimulation during antigen presentation, and the role of cytokine stimulation on effective T cell responses. More specifically, we will address the experimental strategies to interfere with negative co-stimulation such as that mediated by PD-L1 (Programmed cell death 1 ligand 1)/PD-1 (Programmed death 1) to enhance anti-tumour immunity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".